---
title: "Introduction to doseminer"
author: David Selby and Belay Birlie Yimer
date: July 2021
output:
  prettydoc::html_pretty:
    theme: leonids
    df_print: kable
vignette: >
  %\VignetteIndexEntry{introduction}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Motivation

This vignette introduces the R package **doseminer**, a package designed for the task of parsing English freetext prescriptions into a structured format.

Intended end users are researchers in pharmacoepidemiology, especially those using data provided by the [Clinical Practice Research Datalink](https://www.cprd.com) (CPRD), a source of anonymised electronic health data in the United Kingdom.

Electronic prescribing records typically include some structured data, such as the total quantity prescribed, some sort of product code to identify the drug, and the start date of the prescription.

However, the daily dosage may only be available as dispensing instructions in English (and Latin) free text, and sometimes the _length_ of the prescription period can only be inferred from the estimated number of days needed to use up the prescribed total at a particular dose per day.

Pharmacoepidemiologists may wish to estimate the 'exposure window' of a particular drug (i.e. the time that the patient was taking it) to see if there is an association between taking the drug and experiencing adverse events: one example being the relationship between opioid usage and fractures.

## Getting started

Data should be provided as a character (string) vector.
Suppose we have the following dosage instructions (taken from a box of supermarket ibuprofen).

```{r ibuprofen}
p1 <- 'Take 1 or 2 tablets up to 3 times a day, as required'
```

Load the package and use the main workhorse function, `extract_from_prescription`, to parse the data into a structured data frame.

```{r }
library(doseminer)
extract_from_prescription(p1)
```

The output includes seven columns:

- `raw`: the input string. Useful for linking with other data structures
- `dose`: the number of units of drug to administer at once
- `unit`: the units of `dose` (may be unspecified)
- `freq`: the number of times per day that the dose should be administered
- `itvl`: the number of days between 'dose days'; if every day, then 1
- `optional`: an indicator; can the dose be zero? If so, 1, else 0.
<!--- `output`: a 'residual' string of text not parsed by the algorithm. Useful for debugging. May be omitted in future releases.-->

The package is vectorised, so you can provide a vector and it should process all of it, returning the result in the same order as the input.

```{r}
p2 <- c('Take 1 or 2 tablets up to 3 times a day, as required',
        'Swallow 1 or 2 capsules with water, up to three times a day as required.',
        'Two to four 5ml spoonfuls up to 4 times a day')
extract_from_prescription(p2)
```

Names of units, such as millilitre spoonfuls, tablets (tabs) and capsules (caps) are standardised.
Multiplicative doses, for example "two 5ml spoonfuls" are evaluated, giving "10ml spoonful".
Ranges of values are reported as "min--max" in string format.
It is left to the user to decide how to handle such interval data; for example to parse it as a vector, split into columns, compute an average or leave it as a string.

If you do want to divide up such columns then the functions [`separate()`](https://tidyr.tidyverse.org/reference/separate.html) and [`separate_rows()`](https://tidyr.tidyverse.org/reference/separate_rows.html) from the [**tidyr**](https://tidyr.tidyverse.org/index.html) package can come in useful:

```{r}
library(tidyr)
extract_from_prescription(p1) %>%
  separate_rows(freq:dose, convert = TRUE)
```

Here are some more example prescriptions and their output.

```{r}
extract_from_prescription(example_prescriptions)
```

While the package tries to make reasonable inferences about missing data (it's usually fair to assume a dose interval is daily, if not otherwise specified), some variables, especially units, will be returned `NA` if there are no clues in the input text.

## Utilities

In order to parse the prescriptions, the package uses several utilities that have more general applications.
Chief among these is an English number parser.
This can turn individual names of numbers into numeric values:

```{r}
words2number(c('one', 'two', 'three', 'forty two', 'one million'))
```

And it can find and replace such sequences within sentences:

```{r}
replace_numbers(c('I have three apples',
                  'The answer is forty two',
                  'Take one and a half tablets'))
```

However, it does not handle all cases, especially decimals and fractions (except halves).
The digits of these just tend to get added together.

```{r}
words2number(c('three point one four', 'four fifths'))
```

## Improving the package

Like R itself, the package **doseminer** is offered with no warranty.
Use it at your own risk.
If you are interested in helping improve the performance and features of the **doseminer** package, then please file issues and submit pull requests on [GitHub](https://github.com/Selbosh/doseminer).
